International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064




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Informative Article | Software Engineering | India | Volume 8 Issue 9, September 2019 | Rating: 5.8 / 10


Efficiency Boost: Automated Testing and Optimization for Performance Enhancement

Amit Gupta [12]


Abstract: In today's rapidly evolving technological landscape, ensuring the efficiency and stability of software systems through automated performance testing and optimization (APTO) is crucial. This paper presents an overview of the significance of APTO, highlighting its role in enhancing system performance, scalability, and end - user satisfaction. As businesses strive to deliver seamless user experiences amidst fierce competition, software systems' performance directly impacts an organization's success. Traditional performance testing methods often involve manual processes, leading to inefficiencies and human errors, which question the integrity of application developers. APTO emerges as a solution to these challenges, enabling organizations to conduct performance tests rapidly, consistently, and comprehensively across the software development lifecycle. Furthermore, optimization is critical in fine - tuning software systems for peak performance and resource utilization. Organizations can enhance system efficiency and deliver superior user experiences by identifying bottlenecks and optimizing various components. The paper discusses the current methodology for APTO, which encompasses requirement analysis, test planning, environment setup, test script development, execution, monitoring, analysis, optimization, and reporting. This systematic approach ensures software systems meet performance targets and deliver optimal user experiences. Additionally, the paper proposes a mechanism for APTO that leverages emerging technologies, advanced analytics, and automation capabilities. Key components of the proposed mechanism include machine learning - based performance prediction, self - learning test scenario generation, continuous performance monitoring and analysis, dynamic workload management, predicted optimization recommendations, autonomous performance tuning, collaborative platforms, and adaptive governance frameworks.


Keywords: Automated Performance Testing, Performance Optimization, Software Efficiency, Traditional Testing Methods, Machine Learning, Predictive Analytics


Edition: Volume 8 Issue 9, September 2019,


Pages: 1859 - 1863



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